Export Google Maps Reviews to CSV for a Useful Local Audit
Collect a bounded review sample, separate review rows from business summaries, and identify service problems your team can investigate.
A business has hundreds of reviews, yet its next operations meeting still relies on the last complaint someone remembers. A review export gives the team something more consistent to inspect.
Start with a small, clearly defined sample. You can learn whether customers repeatedly mention waiting time, communication, or product quality without pretending the sample represents every customer.
My Google Maps Reviews Scraper exports available review text, ratings, owner responses, and business context from place URLs or Place IDs.
Pick the exact location
Open the intended business in Google Maps and copy its place URL. Check the address, not just the name. A chain's different locations can have very different service problems.
This actor starts from known places. It is not a keyword-based discovery tool for finding every business in a city.
For your first run, enter one place URL, set the per-place review limit to 20, and choose a sort order that matches the question. Keep the interface language at the documented English setting for the current parsing workflow.
Select a sample deliberately
| Research question | Starting sample | Main limitation |
|---|---|---|
| What happened recently? | Newest reviews | Captured dates still need inspection |
| What frustrates reviewers? | Lowest ratings | Deliberately overrepresents negative experiences |
| What do happy reviewers praise? | Highest ratings | Deliberately overrepresents positive experiences |
| What does Google prioritize in this view? | Most relevant | Source ranking is not a random sample |
Record the selected sort and collection time. Keep separate exports for separate sampling strategies so you do not later mistake the merged file for an unbiased survey.
Read the quality summary before the CSV
Open the run's output and inspect each place's dataQualityStatus, reason, and warnings. A successful process can still yield a partial or blocked capture.
The dataset includes business summary records as well as review records. For a review-only spreadsheet, filter recordType to review. Otherwise, a summary row may be counted as another review or introduce misleading empty values.
Preserve the business context and quality summary in a separate tab. The actor documentation lists the current fields and export options.
Build the analysis sheet
Keep these pieces together: review identity, star rating, original text, source date label, available parsed date, owner response, and collection time. Retain a source reference so a colleague can inspect the original context.
Relative labels such as "a month ago" produce approximate dates. Do not turn them into an exact customer-visit timestamp.
Add your own analysis columns:
theme
specific_issue
positive_detail
owner_reply_present
follow_up_question
source_reference
These are review labels you create, rather than claims about the scraper's automatic analysis.
Separate the complaint from the proposed fix
Consider this fictional review note: "The appointment started late, but the staff explained the delay." You might tag both waiting time and communication. A simple negative-sentiment label would lose the useful distinction.
Group similar observations, count the reviews supporting each theme, and include a short evidence note. Then write a question for the business: "Do late appointments cluster in a particular slot?"
The review sample raises a question worth investigating. Internal appointment records would be needed to answer it.
Compare locations using the same method
Keep sort order, time window where available, and sample size comparable. Note each business's overall source rating separately from the average of your exported sample.
Twenty low-rated reviews cannot establish a location's overall satisfaction level. They can help you catalog the problems people chose to describe in those reviews.
When collecting repeatedly, retain observation dates and deduplicate carefully. Review identities may sometimes be generated from available source fields; inspect identity behavior before treating every changed row as a new customer.
Finish with an action memo
Write the three recurring themes, how many sampled reviews support each, the coverage limits, and one internal check the team should perform. Exclude reviewer names when they add nothing to the decision.
If your research also involves mobile products, the app review export guide uses a similar evidence-first workflow. For a local audit, start with one location in the Google Maps Reviews Scraper and verify the first 20 review rows before increasing the scope.